Note: The job is a remote job and is open to candidates in USA. Whatnot is hiring a Software Engineer, Core Machine Learning to build and deploy machine learning models and infrastructure that improve the buyer and seller experience. The role spans the full ML lifecycle, from identifying opportunities and training models to deploying, monitoring, and scaling production systems.
Responsibilities
- Develop and deploy new both production machine learning algorithms and core infrastructure to enrich the buyer and seller experience
- Contribute to and set direction across the entire machine learning development process: opportunity sizing, feature engineering, model training, evaluation, load testing, deployment, and alerting
- Design and implement supporting software infrastructure to unblock your work: streaming pipelines, async worker queues, event enrichment infrastructure, continuous training loops, model monitoring systems, LLM tool access controls, and dynamic agent evaluation framework to give just a few examples
- Define project roadmaps that deliver value quickly while building towards scalable, extensible long-term systems
- Bridge research and production: take an experimental idea and harden it into a scalable, well-tested, monitored system
- Communicate tradeoffs and findings clearly to product and leadership
Skills
- 4+ years of professional experience
- Bachelor's degree in Computer Science, Statistics, Applied Mathematics, or a related technical field, or equivalent work experience
- 2+ years of software engineering experience building and maintaining production systems at consumer scale
- 2+ years of hands-on experience training and deploying traditional ML models in production (e.g. classification, ranking, or regression models using PyTorch, LightGBM, or scikit-learn)
- Ability to work autonomously and drive initiatives across multiple product areas and communicate findings with leadership and product teams
- Experience with operational, search, and key-value databases such as PostgreSQL, DynamoDB, Elasticsearch, Redis
- Firm grasp of visualization tools for monitoring and logging e.g. DataDog, Grafana
- Familiarity with cloud computing platforms and managed services such as AWS Kubernetes, Sagemaker, Lambda, Kinesis, S3, EC2, EKS/ECS, Apache Kafka, Flink
- Exceptional documentation and communication skills
Benefits
- Generous Holiday and Time off Policy
- Health Insurance options including Medical, Dental, Vision
- Work From Home Support
- Care benefits
- 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary)
- Pension plans internationally
- Monthly allowance to dogfood the app
- Parental Leave
- Stock options
- Flexibility to work from home or from one of our global office hubs
Company Overview